Active rule learning using decision tree for resource management in Grid computing

Grid computing is becoming a mainstream technology for large-scale resource sharing and distributed system integration. One underlying challenge in Grid computing is the resource management. In this paper, active rule learning is considered for resource management in Grid computing. Rule learning is very important for updating rules in an active database system. However, it is also very difficult because of a lack of methodology and support. A decision tree can be used in rule learning to cope with the problems arising in active semantic extraction, termination analysis of the rule set and rule updates. Also our aim in rule learning is to learn new attributes in rules, such as time and load balancing, in regard to instances of a real Grid environment that a decision tree can provide. In our work, a set of decision trees is built in parallel on training data sets based on the original rule set. Each learned decision tree can be reduced to a set of rules and thence conflicting rules can be resolved. Results from cross validation experiments on a data set suggest this approach may be effectively applied for rule learning.

Research highlights
► Using a learning system to update resource management rules (ECA rules) to reach adaptive resource management that guarantees to respond to most requests in a real Grid.
► Recognizing which rules should be consolidated, which rules should be modified, and which rules should be invalidated.
► Respond more quickly with a learning system than without a learning system.
► Have a higher performance and be more adaptive with a learning system than without a learning system.